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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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570 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The DECam MAGIC Survey: A Wide-field Photometric Metallicity Study of the Sculptor Dwarf Spheroidal Galaxy

The metallicity distribution function (MDF) and internal chemical variations of a galaxy are fundamental to understand its formation and assembly history. In this work, we analyze photometric metallicities for 3883 stars over 7 half-light radii (rh) in the Sculptor (Scl) dwarf spheroidal (dSph) galaxy, using new narrowband imaging data from the Mapping the Ancient Galaxy in CaHK (MAGIC) survey conducted with the Dark Energy Camera (DECam) at the 4 m Blanco Telescope. This work demonstrates the scientific potential of MAGIC using the Scl dSph galaxy, one of the most well-studied satellites of the Milky Way. Our sample ranges from [Fe/H] ≈ –4.0 to [Fe/H] ≈ –0.6, includes six new extremely metal-poor candidates ([Fe/H] ≤ –3.0), and is almost 3 times larger than the largest spectroscopic metallicity data set in the Scl dSph. Our spatially unbiased sample of metallicities provides a more accurate representation of the MDF, revealing a more metal-rich peak than observed in the most recent spectroscopic sample. It also reveals a break in the metallicity gradient, with a strong change in the slope: from −3.26 ± 0.18 dex deg −1 for stars inside ∼1 rh to −0.55 ± 0.26 dex deg −1 for the outer part of the Scl dSph. Our study demonstrates that combining photometric metallicity analysis with the wide field of view of DECam offers an efficient and unbiased approach for studying the stellar populations of dwarf galaxies in the Local Group.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Modeling supercritical CO 2 flow and mineralization in reactive host rocks with PFLOTRAN v7.0

Understanding the flow and reactivity of CO 2 injected into geological reservoirs is important for many subsurface applications including secure geologic carbon storage (GCS), critical mineral extraction, enhanced geothermal systems (EGS), and enhanced oil recovery (EOR). Traditionally, subsurface CO 2 injection for GCS applications has focused on geologic formations with favorable subsurface configurations for CO 2 migration and trapping through non-reactive mechanisms such as structural, solubility, and petrophysical trapping. Recently, CO 2 -reactive rocks such as mafic and ultramafic basalts have been investigated for their potential to react with injected CO 2 in situ to simultaneously dissolve host rock minerals and mineralize CO 2 as carbonates. Engineering rapid CO 2 mineralization in the subsurface is attractive because of the increased density of stored CO 2 , the additional safety factors associated with solidification, and the potential to extract valuable critical minerals. Here we present recent developments in the parallel flow and reactive transport simulator PFLOTRAN to model coupled CO 2 -brine flow and reactive transport for a wide range of injection and production applications involving reactive CO 2 -brine systems. These developments are based on the well established and trusted CO 2 flow capabilities in the STOMP-CO 2 simulator. New capabilities added to PFLOTRAN include new CO 2 -brine equations of state with optional thermal coupling, several new constitutive relationships like capillary pressure smoothing and scanning path hysteresis, a fully implicit well model, and native linkage with PFLOTRAN's well-established reactive transport libraries. A series of benchmarks between PFLOTRAN and STOMP-CO 2 verify the newly developed CO 2 -brine flow capabilities. Demonstrations of coupled CO 2 -brine flow modeling and reactive transport show how CO 2 mineralization can be engineered in reactive host rocks. Finally, an example use case involving copper leaching by CO 2 and critical mineral extraction is presented to showcase the strengths of this new implementation. Several limitations still remain, including limited availability of field data to parameterize models. Future work should constrain the evolution of mineral surface area during mineralization and the temperature and/or pH dependence of geochemical reactions for specific systems of interest.

Critical Minerals↗
NASA NTRS1977

Alternate approaches to vibration and shock analysis using NASTRAN

A method that derives an approximate equivalent static load to a base excitation shock analysis is described. The transient analysis in the current level of NASTRAN, level 16, does not directly provide for either input acceleration forcing functions or enforced boundary displacement. In the suggested alternate analysis format, equivalent force input functions are applied to the constrained locations by using the artifice of placing a large mass, with respect to the total system mass, at the desired acceleration input points. This shortcut static analysis approach is presented to approximate the expensive and time consuming dynamics analysis approach to the base excitation shock analysis.

Denver, R. E.↗
NASA NTRS2017

The Juno Magnetic Field Investigation

The Juno Magnetic Field investigation (MAG) characterizes Jupiter's planetary magnetic field and magnetosphere, providing the first globally distributed and proximate measurements of the magnetic field of Jupiter. The magnetic field instrumentation consists of two independent magnetometer sensor suites, each consisting of a tri-axial Fluxgate Magnetometer (FGM) sensor and a pair of co-located imaging sensors mounted on an ultra-stable optical bench. The imaging system sensors are part of a subsystem that provides accurate attitude information (to approx. 20 arcsec on a spinning spacecraft) near the point of measurement of the magnetic field. The two sensor suites are accommodated at 10 and 12 m from the body of the spacecraft on a 4 m long magnetometer boom affixed to the outer end of one of 's three solar array assemblies. The magnetometer sensors are controlled by independent and functionally identical electronics boards within the magnetometer electronics package mounted inside Juno's massive radiation shielded vault. The imaging sensors are controlled by a fully hardware redundant electronics package also mounted within the radiation vault. Each magnetometer sensor measures the vector magnetic field with 100 ppm absolute vector accuracy over a wide dynamic range (to 16 Gauss = 1.6 x 10(exp. 6) nT per axis) with a resolution of approx. 0.05 nT in the most sensitive dynamic range (+/-1600 nT per axis). Both magnetometers sample the magnetic field simultaneously at an intrinsic sample rate of 64 vector samples per second. The magnetic field instrumentation may be reconfigured in flight to meet unanticipated needs and is fully hardware redundant. The attitude determination system compares images with an on-board star catalog to provide attitude solutions (quaternions) at a rate of up to 4 solutions per second, and may be configured to acquire images of selected targets for science and engineering analysis. The system tracks and catalogs objects that pass through the imager field of view and also provides a continuous record of radiation exposure. A spacecraft magnetic control program was implemented to provide a magnetically clean environment for the magnetic sensors, and residual spacecraft fields andor sensor offsets are monitored in flight taking advantage of Juno's spin (nominally 2 rpm) to separate environmental fields from those that rotate with the spacecraft.

Juno spacecraft↗
NASA NTRS2017

Observations of Interplanetary Dust by the Juno Magnetometer Investigation

One of the Juno magnetometer investigation's star cameras was configured to search for unidentified objects during Juno's transit en route to Jupiter. This camera detects and registers luminous objects to magnitude 8. Objects persisting in more than five consecutive images and moving with an apparent angular rate of between 2 and 18,000 arcsec/s were recorded. Among the objects detected were a small group of objects tracked briefly in close proximity to the spacecraft. The trajectory of these objects demonstrates that they originated on the Juno spacecraft, evidently excavated by micrometeoroid impacts on the solar arrays. The majority of detections occurred just prior to and shortly after Juno's transit of the asteroid belt. This rather novel detection technique utilizes the Juno spacecraft's prodigious 60 sq. m of solar array as a dust detector and provides valuable information on the distribution and motion of interplanetary (greater than a micron) dust. Plain Language Summary: The Juno magnetometer investigation uses star cameras co-located with the magnetic sensors at the outer end of one of Juno's solar arrays. These cameras compare images with an onboard star catalog to determine the orientation of the sensors in inertial space. They also serendipitously recorded multiple images of small particles excavated from the spacecraft by high-velocity dust impacts. We trace their trajectories back in time to demonstrate that they evolved from the spacecraft. This allows us to use the vast collecting area of Juno's solar arrays (60 sq. m)as a novel dust detector, sensitive to particles with a mass range never before measured in situ.

Jupiter↗
DOE OSTI2020

NDMAS System and Process Description

The U. S. Department of Energy (DOE) has made a significant investment in research to develop the next generation of reactor technologies as well as to improve the performance and lengthen the life cycle of existing nuclear reactors. Data collected to demonstrate new concepts may also be used in the future to support licensing of these technologies. Provenance of these data must be preserved. The Nuclear Data Management and Analysis System (NDMAS) was established to manage and preserve data collected by fuels and materials research conducted by the high-temperature, gas-cooled reactor program. The scope of NDMAS is expanding to include other nuclear research programs that have the shared need to preserve the provenance of research data. Nuclear research funded by DOE is conducted by Idaho National Laboratory (INL), universities, other national laboratories, foreign research partners, and private companies. This research will generate a large amount of data from a variety of sources over a period of many years. Managing the data generated by the research and development projects presents a significant challenge for retaining data integrity and availability.

+↗
DOE OSTI2023

Machine Learning 5G Attack Detection in Programmable Logic

Machine learning-assisted network security may significantly contribute to securing 5G components. However, machine learning network security inference speeds generally require tens to hundreds of milliseconds thereby introducing significant latency in 5G operations. The inference latency can be reduced by deploying the machine learning model to programmable logic in a field programmable gate array (FPGA) at the cost of a small loss in accuracy. In order to quantify this loss, as well as to establish baseline performance inference speeds for programmable logic implementations, this work explores an autoencoder and a ß-variational autoencoder deployed on two different FPGA evaluation boards and compares accuracy and performance against an NVIDIA A100 GPU implementation. A publicly available 5G dataset containing 10 types of attacks along with normal traffic is introduced as part of the evaluation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML) and silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

Gonski, Julia [SLAC]↗
DOE OSTI2026

An improved direct limit on the muon electric dipole moment

A limit on the permanent electric dipole moment (EDM) of the positive muon is presented based on data from the Fermilab Muon g-2 Experiment taken between 2019 and 2020. The tracking detectors measure the average vertical decay angle of positrons from muon decays, enabling a search for an interaction between a possible muon EDM $d_μ$ and the lab-frame magnetic field. The result, $d_μ= (-0.35 \pm 0.19_{\mathrm{stat}} \pm 0.34_{\mathrm{sys}}) \times10^{-19}~e\cdot$cm, is consistent with zero and sets a new direct limit on the muon EDM of $|d_μ|<1.10\times10^{-19}~e\cdot$cm at the 95 percent confidence level.

Aguillard, D. P. [Michigan U.] (ORCID:000000025556↗